QUBO formulation on quantum annealers for joint client selection in federated learning, combined with a MultiSignal routing ensemble, yields higher Byzantine attack detection accuracy than MultiKrum on challenging attacks at both small and moderate scales.
Federated machine learning: Concept and applications
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
Coward detects backdoors in federated learning by injecting a collision-suppressed watermark on OOD data to invert the detection paradigm and limit OOD bias effects.
EdgeDetect delivers 98% multi-class accuracy in federated intrusion detection with 32x gradient compression via median binarization and homomorphic encryption, cutting per-round communication from 450 MB to 14 MB.
citing papers explorer
-
Byzantine-Resilient Federated Learning via QUBO-Based Client Selection on Quantum Annealers
QUBO formulation on quantum annealers for joint client selection in federated learning, combined with a MultiSignal routing ensemble, yields higher Byzantine attack detection accuracy than MultiKrum on challenging attacks at both small and moderate scales.
-
Coward: Collision-based OOD Watermarking for Practical Proactive Federated Backdoor Detection
Coward detects backdoors in federated learning by injecting a collision-suppressed watermark on OOD data to invert the detection paradigm and limit OOD bias effects.
-
EdgeDetect: Importance-Aware Gradient Compression with Homomorphic Aggregation for Federated Intrusion Detection
EdgeDetect delivers 98% multi-class accuracy in federated intrusion detection with 32x gradient compression via median binarization and homomorphic encryption, cutting per-round communication from 450 MB to 14 MB.